import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[str])						->	int:


   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 @property
 def 		__UpperCAmelCase				(		self				:   List[str])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    1
   _UpperCamelCase           =    3
   _UpperCamelCase           =    (32, 32)

   _UpperCamelCase           =    floats_tensor((batch_size, num_channels) + sizes							, rng=random.Random(0)).to(lowercase_)
   return image







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	Optional[Any]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    UNetaDConditionModel(
       block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=4							, out_channels=4							, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							, cross_attention_dim=32							, )
   return model







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    AutoencoderKL(
       block_out_channels=[32, 64]							, in_channels=3							, out_channels=3							, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							, latent_channels=4							, )
   return model







 @property
 def 		__UpperCAmelCase				(		self				:   List[str])						->	List[Any]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    RobertaSeriesConfig(
       hidden_size=32							, project_dim=32							, intermediate_size=37							, layer_norm_eps=1e-0_5							, num_attention_heads=4							, num_hidden_layers=5							, pad_token_id=1							, vocab_size=5006							, )
   return RobertaSeriesModelWithTransformation(lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   str)						->	str:


   """simple docstring"""







   def extract(*lowercase_				:   Optional[int]							, **lowercase_				:   str):




     class 					_UpperCAmelCase			:



      '''simple docstring'''




      def __init__(		self				:   Dict)						->	Union[str, Any]:


        """simple docstring"""







        _UpperCamelCase           =    torch.ones([0])







      def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Dict)						->	Union[str, Any]:


        """simple docstring"""







        self.pixel_values.to(lowercase_)
        return self


     return Out()

   return extract







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"  # ensure determinism for the device-dependent torch.Generator
   _UpperCamelCase           =    self.dummy_cond_unet
   _UpperCamelCase           =    PNDMScheduler(skip_prk_steps=lowercase_)
   _UpperCamelCase           =    self.dummy_vae
   _UpperCamelCase           =    self.dummy_text_encoder
   _UpperCamelCase           =    XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
   _UpperCamelCase           =    77

   _UpperCamelCase           =    self.dummy_image.to(lowercase_)
   _UpperCamelCase           =    init_image / 2 + 0.5

   # make sure here that pndm scheduler skips prk
   _UpperCamelCase           =    AltDiffusionImgaImgPipeline(
       unet=lowercase_							, scheduler=lowercase_							, vae=lowercase_							, text_encoder=lowercase_							, tokenizer=lowercase_							, safety_checker=lowercase_							, feature_extractor=self.dummy_extractor							, )
   _UpperCamelCase           =    VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							, do_normalize=lowercase_)
   _UpperCamelCase           =    alt_pipe.to(lowercase_)
   alt_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    "A painting of a squirrel eating a burger"
   _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(0)
   _UpperCamelCase           =    alt_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=6.0							, num_inference_steps=2							, output_type="np"							, image=lowercase_							, )

   _UpperCamelCase           =    output.images

   _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(0)
   _UpperCamelCase           =    alt_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=6.0							, num_inference_steps=2							, output_type="np"							, image=lowercase_							, return_dict=lowercase_							, )[0]

   _UpperCamelCase           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _UpperCamelCase           =    np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-3







 @unittest.skipIf(torch_device != "cuda"							, "This test requires a GPU")
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.dummy_cond_unet
   _UpperCamelCase           =    PNDMScheduler(skip_prk_steps=lowercase_)
   _UpperCamelCase           =    self.dummy_vae
   _UpperCamelCase           =    self.dummy_text_encoder
   _UpperCamelCase           =    XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
   _UpperCamelCase           =    77

   _UpperCamelCase           =    self.dummy_image.to(lowercase_)

   # put models in fp16
   _UpperCamelCase           =    unet.half()
   _UpperCamelCase           =    vae.half()
   _UpperCamelCase           =    bert.half()

   # make sure here that pndm scheduler skips prk
   _UpperCamelCase           =    AltDiffusionImgaImgPipeline(
       unet=lowercase_							, scheduler=lowercase_							, vae=lowercase_							, text_encoder=lowercase_							, tokenizer=lowercase_							, safety_checker=lowercase_							, feature_extractor=self.dummy_extractor							, )
   _UpperCamelCase           =    VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							, do_normalize=lowercase_)
   _UpperCamelCase           =    alt_pipe.to(lowercase_)
   alt_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    "A painting of a squirrel eating a burger"
   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    alt_pipe(
       [prompt]							, generator=lowercase_							, num_inference_steps=2							, output_type="np"							, image=lowercase_							, ).images

   assert image.shape == (1, 32, 32, 3)







 @unittest.skipIf(torch_device != "cuda"							, "This test requires a GPU")
 def 		__UpperCAmelCase				(		self				:   int)						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/img2img/sketch-mountains-input.jpg")
   # resize to resolution that is divisible by 8 but not 16 or 32
   _UpperCamelCase           =    init_image.resize((760, 504))

   _UpperCamelCase           =    "BAAI/AltDiffusion"
   _UpperCamelCase           =    AltDiffusionImgaImgPipeline.from_pretrained(
       lowercase_							, safety_checker=lowercase_							, )
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)
   pipe.enable_attention_slicing()

   _UpperCamelCase           =    "A fantasy landscape, trending on artstation"

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    pipe(
       prompt=lowercase_							, image=lowercase_							, strength=0.75							, guidance_scale=7.5							, generator=lowercase_							, output_type="np"							, )
   _UpperCamelCase           =    output.images[0]

   _UpperCamelCase           =    image[255:258, 383:386, -1]

   assert image.shape == (504, 760, 3)
   _UpperCamelCase           =    np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




@slow
@require_torch_gpu
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   Dict)						->	Optional[Any]:


   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/img2img/sketch-mountains-input.jpg")
   _UpperCamelCase           =    init_image.resize((768, 512))
   _UpperCamelCase           =    load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy")

   _UpperCamelCase           =    "BAAI/AltDiffusion"
   _UpperCamelCase           =    AltDiffusionImgaImgPipeline.from_pretrained(
       lowercase_							, safety_checker=lowercase_							, )
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)
   pipe.enable_attention_slicing()

   _UpperCamelCase           =    "A fantasy landscape, trending on artstation"

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    pipe(
       prompt=lowercase_							, image=lowercase_							, strength=0.75							, guidance_scale=7.5							, generator=lowercase_							, output_type="np"							, )
   _UpperCamelCase           =    output.images[0]

   assert image.shape == (512, 768, 3)
   # img2img is flaky across GPUs even in fp32, so using MAE here
   assert np.abs(expected_image - image).max() < 1e-2





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
   from .pipeline_kandinsky import KandinskyPipeline
   from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
   from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
   from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
   from .text_encoder import MultilingualCLIP




